Daily Report — 2026-08-13
Daily Overview
- What was done: Developed shin-drag termination and transition rewards for QWOP; analyzed VLM trajectory accuracy for RoboMemory
- How it was done: Used data-driven calibration of physical thresholds via replay analysis and code refactoring in C++/Python
- Impact: Fixed incorrect reward scaling that masked effective training outcomes for QWOP
MacOS
- What was done: Recorded usage statistics only, no active development work performed.
- How it was done: Executed system monitoring commands to track API consumption and usage patterns.
- Impact: Monitored resource utilization without contributing to immediate project goals.
TzJsDesktop
- What was done: Implemented dynamic reward profiles for QWOP RL and inspected VLM evaluation videos for RoboMemory
- How it was done: Refactored C++ reward logic, recalibrated physical thresholds with empirical data, and reviewed rollout recordings
- Impact: Resolved critical bugs in reward scaling enabling valid A/B testing; identified specific failure modes in visual tracking.
Implemented and audited complex reward shaping for QWOP RL training, while analyzing VLM visual tracking failures in RoboMemory.
Tasks
Architecture & Strategy
- ✅ QWOP Reward Shaping Implementation — Implemented shin-drag termination, thigh energy symmetry, and conversion rewards with runtime configurability.
- ✅ QWOP Threshold Calibration — Validated and corrected physical thresholds for shin-drag constraints based on replay data analysis.
Implementation & Fixes
- 🔄 RoboMemory VLM Analysis — Reviewed visual tracking outputs for VLM-win models on PatternLock and RouteStick tasks.
Problems & Solutions
Critical Issues
1. Reward shaping parameters were ineffective during A/B testing due to zeroed coefficients.
Solution: Identified that decay calculations ignored the absolute update count from resumed checkpoints, resetting weights to zero. Added logging to expose scaling status and adjusted logic.
Key Insight: Resumed training contexts require explicit handling of accumulated updates to maintain correct decay curves for hyperparameters.
2. Shin-drag termination criteria failed to trigger because physical coordinates were incorrectly calibrated.
Solution: Analyzed replay data to determine that the threshold was higher than any possible shin Y-position in the environment, rendering it inert. Adjusted based on actual coordinate ranges.
Key Insight: External reward functions from similar environments often have implicit coordinate assumptions that break when physics backends or scales differ.
3. Thigh energy and transition rewards had incorrect semantic logic and scaling.
Solution: Corrected the transition metric to track support foot swaps rather than direct jumps, recalibrated weights against expected magnitude of other terms (like progress).
Key Insight: Reward terms must preserve relative magnitudes; minor semantic errors can amplify into major training bias.
Human vs AI Approaches
Strategic Level
QWOP Physical Validation
| Role | Approach |
|---|---|
| Human | Proposed and executed empirical validation using historical replay data to test hypothesis validity of the implemented features. |
| AI | Attempted to write and run unit tests assuming the code was functionally correct, leading to debugging dead ends. |
Difference Analysis: Human provided critical context about coordinate scale differences that AI missed; Human shifted strategy to data-driven verification when immediate coding failed.
AI Limitations
Critical Limitations
- Failed to infer physical coordinate scaling from project paths, leading to incorrect threshold assumptions for the shin-drag feature.
General Limitations
- Missed the implication of resumed checkpoint updates on decay variable calculations for shaping weights.
- Struggled to directly present large video files, requiring manual retry and path correction steps.
Learnings
Key Learnings
- Always validate external physics parameters against empirical environment data rather than relying on direct code porting.
- Monitor scaling weights and decay coefficients explicitly when resuming training to ensure fair comparisons in A/B testing.
Conversation Summaries
QWOP_rl_training_public
🔄 Implementing and Calibrating Dynamic Reward Profiles for QWOP 23:04:41.521 | claude_code Implemented shin-drag termination, thigh energy terms, and transition rewards. Discovered the initial thresholds were inert due to coordinate scale mismatches; corrected them using replay data analysis. Also found that A/B tests were invalid because shaping weights decayed to zero incorrectly upon resuming from high-update checkpoints.
RoboMemory
🔍 Analyzing VLM Trajectory Failure Signatures in RouteStick Tasks 23:05:06.817 | claude_code Inspected evaluation outcomes for PatternLock and RouteStick tasks. Downloaded and examined video replays of successful and failed episodes to identify two primary failure modes: topological misalignment and tracking monotonicity breakdowns.